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In this paper, we present a novel approach to evolvable embedded systems that is able to adapt to both slow and radical changes in the environment and the system state, respectively. First, a multi-objective evolutionary search algorithm with a selection scheme based on Pareto dominance is used to compute a set of reasonable trade-offs. Then, the decision is made which solution to use for the present situation. During operation, the systems adapts to slowly changing environmental conditions by the evolutionary search process. To handle radical changes, precomputed dominant solutions are stored in the system. When a radical change occurs, the system switches to a good-enough solution, and the online evolutionary process is restarted.
We will present details of the Cartesian Genetic Programming model used, the evolutionary technique, and the evaluation of the fitness with respect to several objectives. We will demonstrate our approach on two classes of applications. The first class of applications reveals an exact correctness measure, where everything less than 100percent correctness is unacceptable. For such a scenario, treating the fitness as a constraint during the optimization process is a viable possibility. The second class of applications relies on a continuous fitness measure, such as the quality of a predictor inside an image compressing algorithm. For such a scenario, the functional quality is best handled as an objective.",
http://klabs.org/mapld06/index.htm",
Genetic Programming entries for Paul Kaufmann Marco Platzner